SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 4925 papers

TitleStatusHype
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
Constraint Guided Model Quantization of Neural Networks—0
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry—0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
A Survey on Deep Hashing Methods—0
A Formalization of Image Vectorization by Region Merging—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Conditional Distribution Quantization in Machine Learning—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—0
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Continuous Control with Action Quantization from Demonstrations—0
Continuous Speech Synthesis using per-token Latent Diffusion—0
A Survey on Learning to Hash—0
Computing with Hypervectors for Efficient Speaker Identification—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified